Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908374283583488 |
|---|---|
| author | Zhu, Wenxuan Wu, Qiyuan Tang, Tengda Meng, Renzi Chai, Sheng Quan, Xuehui |
| author_facet | Zhu, Wenxuan Wu, Qiyuan Tang, Tengda Meng, Renzi Chai, Sheng Quan, Xuehui |
| contents | This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16248 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems Zhu, Wenxuan Wu, Qiyuan Tang, Tengda Meng, Renzi Chai, Sheng Quan, Xuehui Machine Learning This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states. |
| title | Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.16248 |